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This study aims to evaluate the sensing performance of glass fiber-reinforced polymer (GFRP) rock bolt sensors instrumented with strain gauges for monitoring rockslides. Experimental studies are conducted with four different types of GFRP rock bolt sensors and concrete blocks having central holes and two shear joints. Two GFRP rock bolt sensors are inserted into holes and then fixed in concrete blocks with cement grout and soil, respectively. The other two are coated with heat-shrink tubes to protect strain gauges and wires, which are then fixed in concrete blocks with cement grout and soil, respectively. Double shear tests are performed to produce shear deformations of GFRP rock bolt sensors, and then strain change with shear displacement is monitored. The results manifest that the variation in strain with shear displacement is more sensitive in the GFRP rock bolt sensor fixed with soil than with cement grout. Also, strain gauge wires in the GFRP rock bolt sensor fixed with cement grout are broken earlier than with soil. Furthermore, it is confirmed that the heat-shrink tube effectively protects strain gauges and wires, so that GFRP rock bolt sensors coated with heat-shrink tubes work for a longer time than the uncoated sensors. The present study shows that the GFRP rock bolt sensor can be useful for monitoring rock slope failure.

A real-scale slope failure model experiment is performed to analyze the movement behavior of the slope during failure, and the results are analyzed through the x&ndash, MR control chart method, along with inverse displacement and various analysis sections such as K-values. As a result, the portent of failure can be identified to be about 7.7&ndash, 18.3 minutes prior to the final slope failure. As a result of the analyses of changes in the control limit in the various analyses sections, it is considered that the application of K = 3 to the x-MR control chart is effective. It is observed that using the x-MR control chart technique of the inverse displacement is useful for the early prediction of the anomalous behavior of a slope, through a more quick and objective judgment. Henceforth, it is necessary to establish clear techniques for prediction and analyses of slope failure through continuous research, and those results can be used as the basic data of a slope instrumentation management standard that can contribute to the mitigation of life and property damage caused by slope failure hazards.

Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This suggests that there is a need for an early warning system to mitigate casualties and economic losses. Most of the studies on early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient. In this study, a displacement sensor and an Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an inverse displacement analysis was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.